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Journal articles on the topic 'Human keypoint detection'

Author: Grafiati

Published: 25 June 2021

Last updated: 3 February 2022

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1

Zhang, Jing, Zhe Chen, and Dacheng Tao. "Towards High Performance Human Keypoint Detection." International Journal of Computer Vision 129, no. 9 (2021): 2639–62. http://dx.doi.org/10.1007/s11263-021-01482-8.

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2

Gajic, Dusan, Gorana Gojic, Dinu Dragan, and Veljko Petrovic. "Comparative evaluation of keypoint detectors for 3d digital avatar reconstruction." Facta universitatis - series: Electronics and Energetics 33, no. 3 (2020): 379–94. http://dx.doi.org/10.2298/fuee2003379g.

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Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensi

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3

Jeong, Jeongseok, Byeongjun Park, and Kyoungro Yoon. "3D Human Skeleton Keypoint Detection Using RGB and Depth Image." Transactions of The Korean Institute of Electrical Engineers 70, no. 9 (2021): 1354–61. http://dx.doi.org/10.5370/kiee.2021.70.9.1354.

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4

Xu, Ruinian, Fu-Jen Chu, Chao Tang, Weiyu Liu, and Patricio Vela. "An Affordance Keypoint Detection Network for Robot Manipulation." IEEE Robotics and Automation Letters 6, no. 2 (2021): 2870–77. http://dx.doi.org/10.1109/lra.2021.3062560.

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5

Wang, Jue, and Zhigang Luo. "Pointless Pose: Part Affinity Field-Based 3D Pose Estimation without Detecting Keypoints." Electronics 10, no. 8 (2021): 929. http://dx.doi.org/10.3390/electronics10080929.

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Human pose estimation finds its application in an extremely wide domain and is therefore never pointless. We propose in this paper a new approach that, unlike any prior one that we are aware of, bypasses the 2D keypoint detection step based on which the 3D pose is estimated, and is thus pointless. Our motivation is rather straightforward: 2D keypoint detection is vulnerable to occlusions and out-of-image absences, in which case the 2D errors propagate to 3D recovery and deteriorate the results. To this end, we resort to explicitly estimating the human body regions of interest (ROI) and their 3

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Tinchev, Georgi, Adrian Penate-Sanchez, and Maurice Fallon. "SKD: Keypoint Detection for Point Clouds Using Saliency Estimation." IEEE Robotics and Automation Letters 6, no. 2 (2021): 3785–92. http://dx.doi.org/10.1109/lra.2021.3065224.

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7

Apurupa, Leela, J. D.Dorathi Jayaseeli, and D. Malathi. "An Integrated Technique for Image Forgery Detection using Block and Keypoint Based Feature Techniques." International Journal of Engineering & Technology 7, no. 3.12 (2018): 505. http://dx.doi.org/10.14419/ijet.v7i3.12.16168.

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The invention of the net has introduced the unthinkable growth and developments within the illustrious analysis fields like drugs, satellite imaging, image process, security, biometrics, and genetic science. The algorithms enforced within the twenty first century has created the human life more leisurely and secure, however the protection to the first documents belongs to the genuine person is remained as involved within the digital image process domain. a replacement study is planned during this analysis paper to discover. The key plan in the deliberate take a look at and therefore the detect

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T. Psota, Eric, Ty Schmidt, Benny Mote, and Lance C. Pérez. "Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification." Sensors 20, no. 13 (2020): 3670. http://dx.doi.org/10.3390/s20133670.

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Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification ne

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Wang, Yuan-Kai, Hong-Yu Chen, and Jian-Ru Chen. "Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis." Electronics 8, no. 7 (2019): 812. http://dx.doi.org/10.3390/electronics8070812.

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Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illum

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10

Herpers, R., L. Witta, J. Bruske, and G. Sommer. "Dynamic Cell Structures for the Evaluation of Keypoints in Facial Images." International Journal of Neural Systems 08, no. 01 (1997): 27–39. http://dx.doi.org/10.1142/s0129065797000057.

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In this contribution Dynamic Cell Structures (DCS network) are applied to classify local image structures at particular facial landmarks. The facial landmarks such as the corners of the eyes or intersections of the iris with the eyelid are computed in advance by a combined model and data driven sequential search strategy. To reduce the detection error after the processing of the sequential search strategy, the computed image positions are verified applying a DCS network. The DCS network is trained by supervised learning with feature vectors which encode spatially arranged edge and structural i

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11

Jin, Ren, Jiaqi Jiang, Yuhua Qi, Defu Lin, and Tao Song. "Drone Detection and Pose Estimation Using Relational Graph Networks." Sensors 19, no. 6 (2019): 1479. http://dx.doi.org/10.3390/s19061479.

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With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accur

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12

Borgmann, B., M. Hebel, M. Arens, and U. Stilla. "INFORMATION ACQUISITION ON PEDESTRIAN MOVEMENTS IN URBAN TRAFFIC WITH A MOBILE MULTI-SENSOR SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 131–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-131-2021.

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Abstract. This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR’s capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the de

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13

Kanase, Rahul Ravikant, Akash Narayan Kumavat, Rohit Datta Sinalkar, and Sakshi Somani. "Pose Estimation and Correcting Exercise Posture." ITM Web of Conferences 40 (2021): 03031. http://dx.doi.org/10.1051/itmconf/20214003031.

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Our posture shows an impact on health both mentally and physically. Various methods have been proposed in order to detect different postures of a human being. Posture analysis also plays an essential role in the field of medicine such as finding out sleeping posture of a patient. Image processing based and sensor based approach are the leading posture analysis approaches. Sensor based approach is used by numerous models to focus on posture detection in which the person needs to wear some particular devices or sensors which is helpful in cases such as fall detection. Image processing based appr

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14

Kashevnik, Alexey, Walaa Othman, Igor Ryabchikov, and Nikolay Shilov. "Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis." Sensors 21, no. 11 (2021): 3771. http://dx.doi.org/10.3390/s21113771.

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Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displac

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15

Gu, Yanlei, Huiyang Zhang, and Shunsuke Kamijo. "Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network." Sensors 20, no. 6 (2020): 1593. http://dx.doi.org/10.3390/s20061593.

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Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based

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16

Malleson, Charles, John Collomosse, and Adrian Hilton. "Real-Time Multi-person Motion Capture from Multi-view Video and IMUs." International Journal of Computer Vision 128, no. 6 (2019): 1594–611. http://dx.doi.org/10.1007/s11263-019-01270-5.

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AbstractA real-time motion capture system is presented which uses input from multiple standard video cameras and inertial measurement units (IMUs). The system is able to track multiple people simultaneously and requires no optical markers, specialized infra-red cameras or foreground/background segmentation, making it applicable to general indoor and outdoor scenarios with dynamic backgrounds and lighting. To overcome limitations of prior video or IMU-only approaches, we propose to use flexible combinations of multiple-view, calibrated video and IMU input along with a pose prior in an online op

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17

Papakostas, Michalis, Akilesh Rajavenkatanarayanan, and Fillia Makedon. "CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue." Technologies 7, no. 2 (2019): 46. http://dx.doi.org/10.3390/technologies7020046.

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In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-

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18

Wang, Xupeng, Mohammed Bennamoun, Ferdous Sohel, and Hang Lei. "Diffusion Geometry Derived Keypoints and Local Descriptors for 3D Deformable Shape Analysis." Journal of Circuits, Systems and Computers, July 18, 2020, 2150016. http://dx.doi.org/10.1142/s021812662150016x.

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Geometric analysis of three-dimensional (3D) surfaces with local deformations is a challenging task, required by mobile devices. In this paper, we propose a new local feature-based method derived from diffusion geometry, including a keypoint detector named persistence-based Heat Kernel Signature (pHKS), and a feature descriptor named Heat Propagation Strips (HeaPS). The pHKS detector first constructs a scalar field using the heat kernel signature function. The scalar field is generated at a small scale to capture fine geometric information of the local surface. Persistent homology is then comp

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19

Lee, Sarada M. W., Andrew Shaw, Jodie L. Simpson, David Uminsky, and Luke W. Garratt. "Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-96067-3.

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AbstractDifferential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network (“DCNet”), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate gro

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20

Khalifa, Intissar, Ridha Ejbali, Raimondo Schettini, and Mourad Zaied. "Deep Multi-Stage Approach For Emotional Body Gesture Recognition In Job Interview." Computer Journal, April 19, 2021. http://dx.doi.org/10.1093/comjnl/bxab011.

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Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decod

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21

Yang, Zhihui, Xiangyu Tang, Lijuan Zhang, and Zhiling Yang. "A combined local and global structure module for human pose estimation." Journal of Computational Methods in Sciences and Engineering, August 13, 2021, 1–11. http://dx.doi.org/10.3233/jcm-215210.

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Human pose estimate can be used in action recognition, video surveillance and other fields, which has received a lot of attentions. Since the flexibility of human joints and environmental factors greatly influence pose estimation accuracy, related research is confronted with many challenges. In this paper, we incorporate the pyramid convolution and attention mechanism into the residual block, and introduce a hybrid structure model which synthetically applies the local and global information of the image for the analysis of keypoints detection. In addition, our improved structure model adopts g

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